Choose the right source for each claim
Your assistant knows your tastes, but not tonight’s menu
You have a clear request from Module 1. Now imagine the assistant suggests a dish that was removed last week. The missing capability is grounding: connecting a claim to information that supports it.
Retrieval-augmented generation (RAG) means retrieving relevant material and supplying it to a model before it answers. In our project, a menu description can support what a dish contains. A live quote is needed for the current total. A cached description cannot establish either availability or the final price.

Check your understanding
A cached menu lists a dish at $16. What can you safely conclude?
Give evidence an address and a date
Attach a source identifier, an item identifier, and a checked time to evidence. “Menu A, item tofu-bowl, checked at 18:05” is more inspectable than “I read it somewhere.” A timestamp helps you reason about freshness; it is not a guarantee.
Separate relatively stable descriptions from fast-changing stock and charges. Our lab uses fictional, fixed records and marks stale ones. In a real product, define how long each source may be reused and recheck before a consequential action.
Check your understanding
Match each claim to its best evidence.
Make uncertainty visible in the recommendation
A useful recommendation can say: “This menu describes a tofu bowl at $16. I have not verified the current checkout total.” That is a narrower and more useful claim than “This will definitely arrive for $20.”
The Retrieval Lab implements filtering and ranking over sample data. It does not run an embedding model or a generative model. You will inspect which record supports each result before adding those components. Background: retrieving context with its meaning intact.
Check your understanding
Write a recommendation when the menu is known but the final charge is not.
Key takeaways
- Match each claim to the source that can support it.
- Retain source IDs and freshness information.
- Separate menu descriptions from current operational facts.